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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Informatika: Jurnal Pengembangan IT
Septyorini, Talitha Dwi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Dinamika Opini Publik Terkait Quarter Life Crisis Pada Media Sosisal X Menggunakan Support Vector Machine Septyorini, Talitha Dwi; Umam, Khothibul; Handayani, Maya Rini
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8648

Abstract

This study aims to analyze the dynamics of public opinion related to quarter life crisis on platform X through a sentiment analysis approach based on machine learning Support Vector Machine (SVM) algorithm is used to classify positive and negative sentiments from text data. A total of 6.312 tweets were collected with the keyword “quarter life crisis” from January 2024 to January 2025. The data was then processed through the stages of text cleaning, tokenization, stopword removal, stemming, and lexicon-based sentiment labeling. The classification process is carried out using SVM with a data division of 80% training and 20% test. The results showed an accuracy of 81.57% with a sentiment distribution of 59.3% negative and 40.7% positive. Implementation was done on Google Colab platform with evaluation using confusion matrix and classification report. The fingdings prove the effectiveness of SVM in analyzing psychosocial phenomena on social media and provide an empirical basis for the development of digital data-based mental health interventions. The machine learning pipeline optimized in this study can be used as a reference for other studies in analyzing psychological phenomena on social media
Unveiling Public Sentiment on Quarter Life Crisis: A Comparative Performance Evaluation of Support Vector Machine and Naïve Bayes Algorithms on Social Media X Data Septyorini, Talitha Dwi; Umam, Khothibul; Handayani, Maya Rini
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2405

Abstract

Quarter Life Crisis (QLC) is one of the psychological issues experienced by many young adults and is characterized by uncertainty, anxiety, and emotional distress. In the digital era, public opinion about QLC is increasingly expressed through social media, particularly platform X. This study seeks to classify public opinion related to the QLC into positive and negative sentiments by employing two computational classification models, namely Support Vector Machine (SVM) and Naïve Bayes (NB). Despite the growing discourse, there has been no study specifically comparing classification algorithms to analyze public sentiment on QLC. Data collection was conducted through crawling techniques on platform X from November 2024 to January 2025, resulting in a total of 1120 tweets. The data underwent preprocessing, lexicon-based sentiment labeling, and TF-IDF word weighting. After preprocessing, classification using SVM and NB was evaluated by accuracy, precision, recall, and F1-score. Results indicate that SVM achieved superior performance with an accuracy of 83%, outperforming NB, which recorded 74%. These outcomes demonstrate that the SVM algorithm demonstrates superior performance in analyzing public sentiment regarding QLC. This research contributes by providing empirical evidence regarding algorithm performance for sentiment analysis in mental health topics, offering recommendations for effective early detection strategies utilizing social media data.
Co-Authors Khothibul Umam Maya Rini Handayani